Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …

The immense evolution in Large Language Models (LLMs) has underscored the importance
of massive, heterogeneous, and high-quality data. A data recipe is a mixture of data from …

The recent development of Foundation Models (FMs), represented by large language
models, vision transformers, and multimodal models, has been making a significant impact …

Large Language Models (LLMs) are transforming the landscape of mobile intelligence.
Federated Learning (FL), a method to preserve user data privacy, is often employed in fine …

Communication overhead is a significant bottleneck in federated learning (FL), which has
been exaggerated with the increasing size of AI models. In this paper, we propose …

The rapid development of large language models (LLMs) has been witnessed in recent
years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from …

We introduce CYBER-0, the first zero-order optimization algorithm for memory-and-
communication efficient Federated Learning, resilient to Byzantine faults. We show through …

Large language models (LLMs) have achieved remarkable performance in various
downstream tasks. However, training LLMs is computationally expensive and requires a …